Note: In this question you should assume that the activation function of a perceptron is the step function. More specifically, this function: outputs 0 if the weighted sum of inputs is LESS THAN 0 (not less than or equal).
outputs 1 if the weighted sum of inputs is greater than or equal to 0.
Is it possible to design a neural network (which could be a single perceptron or a larger network), that satisfies these specs?
Takes a single input called X, which can be any real number.
If X < 3, the network outputs 0.
If 3 < X < 7, the network outputs 1.
If X > 7, the network outputs 0.
We don't care what the network outputs when X = 3 or X = 7.
If your answer is no, explain why not. If your answer is yes, your solution should include a drawing of the network, that shows the weights of each input of each perceptron, and that also shows which perceptron outputs are linked to which perceptron inputs.